Systems and methods to facilitate automated validation of anti-money laundering alerts

ABSTRACT

Systems and methods to facilitate automated validation of anti-money laundering alerts are disclosed. Exemplary implementations may: obtain alert information indicating monetary transaction flow patterns suspected as money laundering; identify pattern types of the monetary transaction flow patterns; obtain source lists indicating sources of extra-transactional information related to the entities involved in the monetary transactions and significance of the sources; access the sources and obtain the extra-transactional information; evaluate the extra-transactional information included in the sources in accordance with indicated significance of the sources to validate or disprove the suspicions of the monetary transaction flow patterns; and/or perform other operations.

FIELD OF THE DISCLOSURE

The present disclosure relates to systems and methods to facilitateautomated validation of anti-money laundering alerts.

BACKGROUND

Money laundering may involve activities intended to conceal a source ofillegally gotten money. Banks, financial institutes, monitoringagencies, and/or other entities may monitor monetary transaction and/orother activities to determine if money laundering is occurring. Suchmonitoring may be part of anti-money laundering (AML) programs requiredby the government. For example, many AML laws, rules, and orders are inplace pursuant to policies established by the Securities and ExchangeCommission (SEC). These monitoring programs will issue alerts if itsdetermined that one or more monetary transactions appear suspicious.

SUMMARY

Aspects of the present disclosure relates to systems and methodsconfigured to facilitate automated validation of anti-money launderingalerts. Anti-money laundering (AML) alerts may be obtained from one ormore entities that monitor monetary transactions and generate thealerts. By way of non-limiting illustration, alerts may be obtained froma transaction monitoring system and/or other systems included in banks,financial institutes, monitoring agencies, and/or other entities thatgenerates AML alerts. The alerts may convey suspicions that one or moremonetary transactions appear as money laundering. One or moreimplementations of the systems and methods presented herein may bedirected to validating and/or disproving these suspicions by evaluatingextra-transactional information, e.g., information about the entitiesinvolved in the monetary transactions but not necessary informationabout the transactions themselves. In particular, theextra-transactional information may include publicly availableinformation which may be ranked based on significance of thatinformation in its use to validate and/or disprove the suspicions.

The system may include one or more processors configured bymachine-readable instructions. The machine-readable instructions mayinclude one or more computer program components. The one or morecomputer program components may include one or more of an alertcomponent, a pattern type component, a source list component, a sourceaccessing component, an evaluation component, and/or other components.

The alert component may be configured to obtain alert information and/orother information. The alert information may include alerts indicatingone or more of monetary transaction flow patterns suspected as moneylaundering, entities involved in monetary transactions that contributeto the monetary transaction flow patterns, and/or other information.

The pattern type component may be configured to identify pattern typesof the monetary transaction flow patterns.

The source list component may be configured to obtain source listsindicating sources of extra-transactional information. An individualsource list may be specific to an individual pattern type. Theextra-transactional information may include information about theentities involved in the monetary transactions. An individual sourcelist may further indicate significance of the individual sourcesincluded in the individual source list.

The source accessing component may be configured to access the sourcesand/or obtain the extra-transactional information in the sources thatmay be related to the entities involved in the monetary transactions.

The evaluation component may be configured to evaluate theextra-transactional information included in the sources in accordancewith the indicated significance of the sources. The evaluation mayfacilitate validating or disproving the suspicions of the monetarytransaction flow patterns indicated in the alerts. The indicatedsignificance of the sources may convey weights applied to the sources indetermining whether to validate or disprove of the suspicions.

These and other features, and characteristics of the present technology,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the invention. As usedin the specification and in the claims, the singular form of “a”, “an”,and “the” include plural referents unless the context clearly dictatesotherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system configured to facilitate automatedvalidation of anti-money laundering alerts, in accordance with one ormore implementations.

FIG. 2 illustrates a method to facilitate automated validation ofanti-money laundering alerts, in accordance with one or moreimplementations

FIG. 3 shows a graphical illustration of a source list, illustrating oneor more of sources ranked by significance, types of extra-transactionalinformation within individual sources ranked by significance, and/or theextra-transactional information within the individual sources ranked bysignificance.

DETAILED DESCRIPTION

FIG. 1 illustrates a system 100 configured to facilitate automatedvalidation of anti-money laundering alerts, in accordance with one ormore implementations. Anti-money laundering (AML) alerts may be obtainedfrom a transaction monitoring system and/or other systems included inbanks, financial institutes, monitoring agencies, and/or other entitiesthat generate the AML alerts. The alerts may convey suspicions that oneor more monetary transactions appear as money laundering. One or moreimplementations of the systems and methods presented herein may bedirected to validating and/or disproving these suspicions by evaluatingextra-transactional information and/or other information.

In some implementations, system 100 may include one or more of one ormore servers 102, one or more client computing platforms 104, one ormore sources 120, and/or other components. Server(s) 102 may beconfigured to communicate with one or more client computing platforms104 according to a client/server architecture and/or otherarchitectures. Client computing platform(s) 104 may be configured tocommunicate with other client computing platforms via server(s) 102and/or according to a peer-to-peer architecture and/or otherarchitectures. Users may access system 100 via client computingplatform(s) 104. Communications may be facilitated through network(s)122. The network(s) 122 may include wired and/or wireless connections.The network(s) 122 may include the Internet, and/or other communicationnetworks. It will be appreciated that this is not intended to belimiting and that the scope of this disclosure includes implementationsin which components of system 100 may be operatively linked via someother communication media.

Server(s) 102 may include one or more physical processor 130 configuredby machine-readable instructions 106. Executing the machine-readableinstructions 106 may cause one or more physical processors 130 ofserver(s) 102 to facilitate automated validation of anti-moneylaundering alerts. Machine-readable instructions 106 may include one ormore computer program components. The computer program components mayinclude one or more of an alert component 110, a pattern type component112, a source list component 114, a source accessing component 116, anevaluation component 118, and/or other computer program components.

Alert component 110 may be configured to obtain alert information and/orother information. The alert information may include alerts and/or otherinformation. The alerts may indicate one or more of monetary transactionflow patterns suspected as money laundering, identifications of entitiesinvolved in monetary transactions that contribute to the monetarytransaction flow patterns, and/or other information. The alerts mayinclude anti-money laundering alerts, or “AML” alerts. Money launderingmay refer to money concealment. Money laundering may include concealingthe source of illegally gotten money, according to some implementations.

The entities involved in monetary transaction may include one or more ofa beneficiary of a monetary transaction, a sender of a monetarytransaction, an intermediary in a monetary transaction, and/or otherentities that may be involved in monetary transactions. An entity may beone or more of a corporate entity, an individual (e.g., a person), agroup of people, and/or other entities.

By way of non-limiting illustration, alert component 110 may beconfigured to obtain alert information including one or more of a firstalert indicating a first monetary transaction flow pattern suspected asmoney laundering, identification of a first entity and/or other entitiesinvolved in one or more monetary transactions that contribute to thefirst monetary transaction flow pattern, and/or other information.

Pattern type component 112 may be configured to identify pattern typesof the monetary transaction flow patterns indicated in the alerts. Thepattern types may refer to techniques in which entities may act toconceal the source of illegally gotten money. The pattern types may bedetermined based on the alert information and/or other information. Byway of non-limiting illustration, the pattern types may be determinedbased on matching the monetary transactions that contribute to themonetary transaction flow patterns to the type of transactionsassociated with individual pattern types. In some implementations, thealert information may indicate the pattern types, e.g., the patterntypes may be indicated in the received AML alerts.

In some implementations, the pattern types may include one or more of amany-to-one type, a receiving velocity type, a low chargeback type, asales volume mismatch type, and/or other types.

The many-to-one type monetary transaction flow pattern may refer tomoney laundering cases where individual transactions and/or series oftransactions have one or more common points of origin and a commonbeneficiary. For example, multiple common points of origin may besending money to a common beneficiary. The multiple common points oforigin may be acting in concert to conceal the illegal nature of moneybeing sent to the common beneficiary. The common point of origin mayrefer to one or more of region of origin, an originating financialaccount, an originating institution, and/or other point of origin. Theregion may be a location. The region may include the extended spatiallocation, according to some implementations.

The receiving velocity type monetary transaction flow pattern may referto cases where a velocity of money received by an entity meets and/orexceeds a threshold. This may mean the entity has more incoming moneythan may be typically expected. In some implementations, the thresholdmay be relative to one or more of the business or industry the receivingentity is involved in, the location of the entity, and/or otherinformation. The velocity of money received by an entity may include arate at which the money is received by the entity. In someimplementations, the velocity of money received by an entity may berelative to a period of time. In some implementations, the period oftime may be one or more of one day, multiple days, one week, multipleweeks, one month, multiple months, one year, multiple year, and/or otherperiods of time.

The low chargeback type monetary transaction flow pattern may refer tocases where a demand by a credit-card provider for an entity to makegood the loss on a fraudulent or disputed transaction meets and/or fallsbelow a threshold. Chargebacks may be typical in conventional, legalbusinesses. If there the chargebacks are relatively low (compared tothreshold), this may cause suspicion because it shows that substantialamount of transaction are occurring without any issue, which may not becommon. In some implementations, the threshold may be relative to one ormore of the business or industry the entity is involved in, the locationof the entity, and/or other information. In some implementations, anevaluation of the chargeback may be relative to a period of time. Insome implementations, the period of time may be one or more of one day,multiple days, one week, multiple weeks, one month, multiple months, oneyear, multiple year, and/or other periods of time.

The sales volume mismatch type monetary transaction flow pattern mayrefer to cases where an asserted volume of sales by an entity does notmeet, or substantially match, a social and/or economic profile for thatentity. This may cause suspicion because the entity may be reportingmore sales (e.g., fraudulent sales) than are actually occurring. Asocial profile may be derived from sources of social information,described in more detail herein. An economic profile may be derived fromother public sources that may provide economic insight for the entity.For example, the other public sources where an economic profile may bederived for the entity may include public earnings and sales reports,and/or other sources.

It is noted that the above examples of pattern types of monetarytransaction flow patterns that causes suspicion of money laundering isfor illustrative purposes and not to be considered limiting. Forexample, other patterns and/or techniques in which entities may act toconceal the source of illegally gotten money are contemplated within thescope of this disclosure.

By way of non-limiting illustration, pattern type component 112 may beconfigured to identify a first pattern type of the first monetarytransaction flow pattern of the first alert.

Source list component 114 may be configured to obtain source listsindicating sets of sources included in one or more sources 120. The oneor more sources 120 may be sources of extra-transactional informationand/or other information. The extra-transactional information mayinclude information about the one or more entities involved in monetarytransactions. The extra-transactional information may include publiclyavailable information. The extra-transactional information may includeinformation about the one or more entities involved in monetarytransactions that may be different from information about thetransactions themselves. By way of non-limiting illustration,information about the transactions themselves may be private information(e.g., bank information) and the extra-transactional information may bepublic information and/or other information.

The sources in the source lists may be used to evaluate the suspicionsof the monetary transaction flow patterns to determine whether thosesuspicions are warranted. For example, evaluation of individual sourcesmay facilitate validating and/or disproving such suspicions. Forexample, it may be possible that an alert indicating a suspiciousmonetary transaction flow pattern was a “false alarm.” That is, anentity may have engaged in activities that appear to be money launderingwhich causes a generation of an alert. However, it may be possible thatthe entity was merely engaging in legal activities that happened tofollow a monetary transaction flow pattern that raises suspicions ofmoney laundering and/or other fraud. The evaluation ofextra-transactional information may provide a technique to validate ordisprove the suspicions using information that may not be directly tiedto the transactions that contributed to that suspicious patterns.

An individual source list may be specific to one or more pattern types.An individual source list may be specific to an individual pattern type.For example, a source list may include a compilation of sourcesdetermined to provide the best insight as to validate or disprove analert conveying a particular type of monetary transaction flow pattern.By way of non-limiting illustration, the first pattern type may beassociated with a first source list, a second pattern type may beassociated with a second source list, and/or other pattern types may beassociated with other source lists.

In some implementations, different source lists may have one or moresources that are common between them. In some implementations, differentsource lists may include different sources. In some implementations, asource list may include at least one source that may not be included inanother source list. Once a pattern type is determined, an source listspecific to that pattern type may be obtained and extra-transactionalinformation and/or other information included in the sources of thesource list may be accessed and evaluated to validate or disprove asuspicion.

In some implementations, an individual source list may indicatesignificance of the individual sources included in the individual sourcelist. The significance may be an importance of the information as itrelates to determining whether or not extra-transactional information ofthe source may be indicative of money laundering (see, e.g., evaluationcomponent 118). For example, individual sources in an individual sourcelist may be ranked based on order of significance. The evaluation ofextra-transactional information within individual sources may beweighted based on respective position in the rank. Examples of thesignificance may include one or more of consequence of that informationas its related to validating and/or disproving suspicions,historicalness of that information as its related to validating and/ordisproving suspicions, meaningfulness of that information as its relatedto validating and/or disproving suspicions, and/or other significancesas it relates to determining whether or not extra-transactionalinformation may be indicative of money laundering.

In some implementations, individual source lists may further indicate,for individual sources in the individual source list, significance oftypes of extra-transactional information included in the individualsources. For example, individual sources may include one or more typesof extra-transactional information about an entity. Accordingly, in someimplementations, not only may the sources within a source list beranked, but the types of information included within individual sourcesmay be rank based on the type's respective significance.

In some implementations, individual source lists may further indicate,for individual sources in the individual source list, significance ofindividual extra-transactional information included in the individualsources. The significant of individual extra-transactions informationmay be independent of the type of the extra-transactional informationand/or may be in accordance with the ranking of the type. Accordingly,in some implementations, not only may the sources within a source listbe ranked and/or the types of extra-transactional information withinindividual sources be ranked, but the extra-transactional informationitself included within individual sources may be rank based onrespective significance.

FIG. 3 illustrates a graphical illustration of a source list 300. Thesource list 300 illustrating one or more of sources ranked bysignificance, types of extra-transactional information within individualsources ranked by significance, and/or the extra-transactionalinformation within the individual sources ranked by significance. Therank is graphically shown in a descending order from the top of the pagetoward the bottom where a higher position represents a relatively higherrank and/or higher significance, and the lower position represents arelatively lower rank and/or a lower significance.

By way of non-limiting illustration, the first column (e.g., the ovals)may represent sources ranked by significance. The sources may includeone or more of a first source 302, a second source 304, a third source306, and/or other sources. The first source 302 may be more significantthan second source 304, second source 304 may be more significant thanthird source 306, and so on. The second column (e.g., the rectangles)may represent how types of extra-transactional information within thesources and/or the extra-transactional information within the sourcesmay be ranked by significance. In some implementations, elements 308-316may represent types of extra-transactional information within firstsource 302 ranked by significance. In some implementations, elements308-316 may represent the extra-transactional information within firstsource 302 ranked by significance. In some implementations, elements 318and 320 may represent types of extra-transactional information withinsecond source 304 ranked by significance. In some implementations,element 322 may represent a type of extra-transactional informationwithin third source 306 determined as being significant. In someimplementations, element 322 may represent extra-transactionalinformation within third source 306 determined as being significant.

Returning to FIG. 1, in some implementations, one or more sources 120may include one or more of a social media source, a customer duediligence source, an employment information source, a credit cardinformation source, a velocity of funds reports source, an earnings andsales reports source, and/or other sources. It is noted that thesources, types of extra-transactional information included within thesources, extra-transactional information included within those types,and/or significances of the sources, types, and extra-transactionalinformation presented herein are for illustrative purposes and not to beconsidered limiting. It is noted that the sources and/or types ofextra-transactional information included therein is provided forillustrative purposes only and is not intended to be limiting. Instead,it is to be understood that other sources of extra-transactionalinformation, other types of extra-transactional information, and/orother extra-transactional information may be contemplated within thescope of this disclosure.

A social media source may include a source of publicly availableinformation. Social media sources may provide insight as to arelationship between a sender and a receiver of a monetary transaction.The social media sources may include one or more of Yelp®, Facebook®,Twitter®, Google® Maps, LinkedIn®, and/or other social media sources. Insome implementations, types of extra-transactional information in thesocial media sources may include one or more of crowd-sourced reviewinformation, engagement information, identification information, and/orother information.

The crowd-sourced review information may be related to informationgathered from the public's activity with a social media source of anentity. By way of non-limiting illustration, the crowd-resourced reviewinformation may include one or more of quantity of reviews, quantity ofshares, average review length, average photos per review, distributionof reviews over time, quantity of up-votes, average votes per review,average quantity of reviews done by individual reviewers, quantity ofstars, information establishing whether reviewers are real people,quantity of visits, average comments on posts, quantity of followers,quantity of check-ins, and/or other information.

In some implementations, the engagement information may be related toactivities of an entity to reach out to, or engage with, the public viaa social media source. By way of non-limiting illustration, theengagement information may include one or more of quantity of photos,frequency of posts, information in an about-us page, length of anabout-us page, and/or other information.

In some implementations, the identification information may beassociated with information that identifies an entity and/or may bespecific to an identity of an entity. By way of non-limitingillustration, the identification information may include one or more ofage of a social media profile, listed address, listed phone number,listed hours, listed website, business category or company type, companysize, price range of products and/or services offered, year founded,specialty offerings, friends or connections with the social mediaprofile, description of work experience, lists of company employees,length of biography on the profile (e.g., an “about us” description),and/or other information.

In some implementations, a customer due diligence source may refer to asource that include publicly available personal information aboutentities. The types of the extra-transactional information in customerdue diligence sources may include one or more of beneficiaryinformation, point-of-origin information, driver's license information,criminal background information, and/or other information. A customerdue diligence source may include one or more of libraries, DMV records,court records, websites (e.g., commercial, business, and/or personal),Business directories, regulatory filings, and/or other sources.

The beneficiary information may be information about an entity whoreceives money in a monetary transaction. The information about abeneficiary may include information that identifies an entity and/orinformation that may be specific to an identity of an entity. By way ofnon-limiting example, the beneficiary information may include one ormore of entity location (e.g., location of primary place of business),age of the entity (e.g., age of business), business status (e.g., one ormore of LLC, partnership, corporation, LLP, and/or other status),region, industry, length of relationship to point-of-origin of amonetary transaction that benefits the beneficiary, financial status(e.g., one or more of debt, credit rating, and/or other status), and/orother information. An industry may be a commercial enterprise. Theindustry may include the people or companies engaged in a particularkind of commercial enterprise, according to some implementations.Examples of the industry may include one or more of aluminum business,apparel industry, automobile industry, aviation, banking industry,chemical industry, coal industry, computer industry, constructionindustry, electronics industry, entertainment industry, film industry,growth industry, lighting industry, market, munitions industry, oilindustry, plastics industry, service industry, shipbuilding industry,shoe industry, sign industry, steel industry, tobacco industry, toyindustry, trucking industry, and/or other industries.

The point-of-origin information may be information about a sendingentity in a monetary transaction. The information about a sender mayinclude information that identifies an entity and/or may be specific toan identity of an entity. By way of non-limiting example, thepoint-of-origin information may include one or more of location,business status, region, industry, age of the entity (e.g., age ofbusiness), and/or other information.

The driver's license information may be associated with driver's licenseand/or other public driving records of an entity. By way of non-limitingillustration, the driver's license information may include one or moreof a driver's license number, a state of issuance, a status of license(e.g., issued, pending, revoked, and/or other status), and/or otherinformation.

The criminal background information may include publicly availablecriminal information about an entity involved in a monetary transaction.The criminal background information may include information about pastinvolvement in high money laundering crimes and/or other crimes. By wayof non-limiting example, the crimes may include one or more of druginvolvement, terrorism involvement, gang involvement, and/or othercrimes that may be tied to money laundering.

An employment information source may include public information about anentity's past and/or current employment. The types of theextra-transactional information in an employment information source mayinclude one or more of salary information, current employer information,length of current employment information, past employer information,length of past employment information, and/or other information. Anemployment information source may include one or more of libraries,websites (e.g., commercial, business, and/or personal), professionallicensing records, regulatory filings, and/or other sources.

The salary information may include salary of an entity. The currentemployer information may include one or more of a name of a currentemployer, an industry of a current employer, and/or other information.The length of current employment information may include a length of acurrent employment. The past employer information may include one ormore of a name of a past employer, an industry of a past employer,and/or other information. The length of past employment information mayinclude a length of a past employment.

A credit card information source may include a source of publiclyavailable credit and/or credit history information. In someimplementations, a type of the extra-transactional information in acredit card information source may include transaction historyinformation and/or other information. By way of non-limiting example,the transaction history information may include one or more of quantityof transactions, quantity of chargebacks, average quantity ofchargebacks for an individual business (e.g., a similar business to abusiness of an entity indicated in an alert), size of the individualbusiness, industry of the individual business, age of the individualbusiness, and/or other information. The credit card information sourcemay include one or more of transactional data databases, risk databasesand/or other sources.

A velocity of funds reports source may include sources of publiclyavailable information related to purported funds of an entity. In someimplementations, a type of the extra-transactional information in avelocity of funds report source may include velocity of fundsinformation and/or other information. The velocity of funds informationmay include a velocity of funds report and/or other information. Thevelocity of funds reports source may include one or more of the entitythemselves, a public reporting entity, a risk database, and/or othersources.

An earnings and sales reports source may include a source of publiclyavailable information related to purported earnings, sales, and/or otherfigures related to a business of an entity. In some implementations, atype of the extra-transactional information in an earnings and salesreports source may include earnings and sales information and/or otherinformation. The earnings and sales information may include an earningsand sales report and/or other information. The earnings and salesreports source may include one or more of the entity themselves, apublic reporting entity, business databases (e.g., databases providingestimates such as TechCrunch®), news databases and/or other sources.

In some implementations, a source list specific to the many-to-one typemonetary transaction flow pattern may include one or more of socialmedia sources, customer due diligence sources, employment informationsources, and/or other sources. In some implementations, the source listspecific to the many-to-one type monetary transaction flow pattern mayspecify significance of the sources in the source list. In someimplementations, the source list specific to the many-to-one typemonetary transaction flow pattern may specify that the customer duediligence sources may be more significant than employment informationsources, and/or that employment information sources may be moresignificant than the social media sources.

In some implementations, the source list specific to the many-to-onetype monetary transaction flow pattern may specify significance of thetypes of extra-transactional information included in the individualsources and/or significance of the extra-transactional informationitself.

For the customer due diligence sources, the source list specific to themany-to-one type monetary transaction flow pattern may specifysignificance of types of extra-transactional information in the customerdue diligence sources. By way of non-limiting illustration, the sourcelist may specify that the beneficiary information may be moresignificant than the point-of-origin information, the point-of-origininformation may be more significant than the driver's licenseinformation, and/or the driver's license information may be moresignificant than the criminal background information.

For the beneficiary information specific to the many-to-one typemonetary transaction flow pattern, the location may be more significantthan the business status, the business status may be more significantthan the business region, the business regions may be more significantthan the industry, the industry may be more significant than the lengthof relationship to a point-of-origin, and/or the length of relationshipto a point-of-origin may be more significant than the financial status.

For the point-of-origin information specific to the many-to-one typemonetary transaction flow pattern, the location may be more significantthan the business status, the business status may be more significantthan the business region, and/or the business region may be moresignificant than the industry.

For employment information sources, the source list specific to themany-to-one type monetary transaction flow pattern may specifysignificance of the types of extra-transactional information included inthose sources. For example, the source list may specify that the salaryinformation may be more significant than current employer information,the current employer information may be more significant than length ofcurrent employment information, the length of current employment may bemore significant than past employer information, and/or the pastemployer information may be more significant than length of pastemployment information.

For social media sources, the source list specific to the many-to-onetype monetary transaction flow pattern may specify one or more ofFacebook® as a social media source, LinkedIn® as a social media source,and/or other sources. The source list specific to the many-to-one typemonetary transaction flow pattern may specify one or more ofsignificance of the different social media sources, significance of thetypes of extra-transactional information in the social media sources,and/or significance of the extra-transactional information. By way ofnon-limiting illustration, the source list specific to the many-to-onetype monetary transaction flow pattern may specify that Facebook® may bemore significant than LinkedIn®.

For types of extra-transactional information included in social mediasources, the source list specific to the many-to-one type monetarytransaction flow pattern may specify that identification information maybe most significant. For the identification information included inFacebook®, the source list specific to the many-to-one type monetarytransaction flow pattern may specify that description of work experienceand/or friends or connections within the social media profile may besignificant. The source list may specify that the description of workexperience may be more significant than friends or connections with thesocial media profile on Facebook®. For the identification informationincluded in LinkedIn®, the source list specific to the many-to-one typemonetary transaction flow pattern may specify that description of workexperience and/or listing of company employees may be significant. Thesource list may specify that listing of company employees may be moresignificant than the description of work experience.

In some implementations, a source list specific to the receivingvelocity type monetary transaction flow pattern type may include one ormore of credit card information sources, customer due diligence sources,velocity of funds reports sources, social media sources, and/or othersources. In some implementations, the source list specific to thereceiving velocity type monetary transaction flow pattern may specifysignificance of the sources in the source list. In some implementations,the source list specific to the receiving velocity type monetarytransaction flow pattern may specify that the credit card informationsources may be more significant than the customer due diligence sources,the customer due diligence sources may be more significant than thevelocity of funds reports sources, and/or the velocity of funds reportsources may be more significant than the social media sources.

In some implementations, the source list specific to the receivingvelocity type monetary transaction flow pattern may specify the types ofextra-transactional information that may be significant, thesignificance of the types of extra-transactional information included inthe individual sources, and/or significance of the differentextra-transactional information.

For the credit card information sources, the source list specific to thereceiving velocity type monetary transaction flow pattern may specifythat the quantity of transactions may be most significant.

For the customer due diligence sources, the source list specific to thereceiving velocity type monetary transaction flow pattern may specifythat beneficiary information and driver's license information may besignificant. For the beneficiary information, the source list mayspecify that age of the entity and/or the industry may be significant.The source list specific to the receiving velocity type monetarytransaction flow pattern may specify that the beneficiary informationmay be more significant than driver's license information. For thebeneficiary information, the source list may specify that the age of theentity may be more significant than the industry.

For social media sources, the source list specific to the receivingvelocity type monetary transaction flow pattern may specify one or moreof Yelp® as a social media source, Facebook® as a social media source,Twitter® as a social media source, Google® Maps as a social mediasource, LinkedIn® as a social media source, and/or other sources. Thesource list specific to the receiving velocity type monetary transactionflow pattern may specify one or more of significance of the differentsocial media sources, significance of the types of extra-transactionalinformation in the social media sources, and/or significance of theextra-transactional information. By way of non-limiting illustration,the source list specific to the receiving velocity type monetarytransaction flow pattern may specify that Yelp® may be more significantthan Facebook®, Facebook® may be more significant than Twitter®,Twitter® may be more significant than Google® Maps, and/or Google® Mapsmay be more significant than LinkedIn®.

For extra-transactional information included in Yelp®, the source listspecific to the receiving velocity type monetary transaction flowpattern may specify one or more of: quantity of reviews may be moresignificant than average review length, average review length may bemore significant than quantity of photos, quantity of photos may be moresignificant than average photos per review, average photos per reviewmay be more significant than age of profile, age of profile may be moresignificant than distribution of reviews over time, distribution ofreviews over time may be more significant than quantity of stars,quantity of stars may be more significant than identificationinformation, the identification information may be more significant thanaverage votes per review, average votes per review may be moresignificant than average quantity of reviews done by individualreviewers, and/or average quantity of reviews done by individualreviewers may be more significant than quantity of check-ins.

For extra-transactional information included in Facebook®, the sourcelist specific to the receiving velocity type monetary transaction flowpattern may specify one or more of: quantity of reviews may be moresignificant than average review length, average review length may bemore significant than quantity of photos, quantity of photos may be moresignificant than quantity of likes (and/or followers and/or visits),quantity of likes (and/or followers and/or visits) may be moresignificant than identification information (including one or more ofaddress, phone number, website, hours, business category, and/or pricerange), the identification information may be more significant thaninformation establishing whether reviewers are real people, informationestablishing whether reviewers are real people may be more significantthan average likes on posts (and/or comments on posts), the averagelikes on posts (and/or comments on posts) may be more significant thanfrequency of posts, and/or the frequency of posts may be moresignificant than age of profile.

For extra-transactional information included in Twitter®, the sourcelist specific to the receiving velocity type monetary transaction flowpattern may specify one or more of: age of profile may be moresignificant than quantity of tweets, quantity of tweets may be moresignificant than quantity of retweets, and/or quantity of retweets maybe more significant than quantity of likes.

For extra-transactional information included in Google® Maps, the sourcelist specific to the receiving velocity type monetary transaction flowpattern may specify one or more of: listed hours may be more significantthan listed address, listed address may be more significant than listedphone number, listed phone number may be more significant than a listedwebsite link, listed website may be more significant than quantity ofreviews, quantity of reviews may be more significant than averagequantity of ratings, average quantity of rantings may be moresignificant than quantity of photos, and/or quantity of photos may bemore significant than average length of reviews.

For extra-transactional information included in LinkedIn®, the sourcelist specific to the receiving velocity type monetary transaction flowpattern may specify one or more of: length of biography may be moresignificant than a listed website, the listed website may be moresignificant than a listed location, listed location may be moresignificant than year founded, year founded may be more significant thancompany type, company type may be more significant than company size,company size may be more significant than specialty offerings, specialtyofferings may be more significant than posted job offerings, posted jobofferings may be more significant than new hires per month, new hiresper month may be more significant than total job openings posted, and/ortotal job opening posted may be more significant than average employeetenure.

In some implementations, a source list specific to the low chargebacktype monetary transaction flow pattern type may include one or more ofcredit card information sources, earnings and sales reports sources,and/or other sources. In some implementations, the source list specificto the low chargeback type monetary transaction flow pattern may specifysignificance of the sources in the source list. In some implementations,the source list specific to the low chargeback type monetary transactionflow pattern may specify that the credit card information sources may bemore significant than the earnings and sales reports sources.

In some implementations, the source list specific to the low chargebacktype monetary transaction flow pattern may specify the types ofextra-transactional information that may be significant, thesignificance of the types of extra-transactional information included inthe individual sources, and/or significance of the differentextra-transactional information.

For the credit card information sources, the source list specific to thelow chargeback type monetary transaction flow pattern may specify thatquantity of transactions, quantity of chargebacks, and/or averagequantity of chargebacks for a similar business may be significant. Thesource list may specify significance of the extra-transactionalinformation. By way of non-limiting illustration, the source list mayspecify that quantity of transactions may be more significant than thequantity of chargebacks, and/or that the quantity of chargebacks may bemore significant than the average quantity of chargebacks for a similarbusiness.

For the earnings and sales reports sources, the source list specific tothe low chargeback type monetary transaction flow pattern may specifythat earnings and sales reports may be significant. In someimplementations, the earnings and sales reports included in the earningsand sales reports sources may be the only significantextra-transactional information.

In some implementations, a source list specific to the sales volumemismatch type monetary transaction flow pattern type may include one ormore of earnings and sales reports sources, social media sources, and/orother sources. In some implementations, the source list specific to thesales volume mismatch type monetary transaction flow pattern may specifysignificance of the sources in the source list. In some implementations,the source list specific to the sales volume mismatch type monetarytransaction flow pattern may specify that the earnings and sales reportssources may be more significant than the social media sources.

In some implementations, the source list specific to the sales volumemismatch type monetary transaction flow pattern may specify the types ofextra-transactional information that may be significant, thesignificance of the types of extra-transactional information included inthe individual sources, and/or significance of the differentextra-transactional information.

For the earnings and sales reports sources, the source list specific tothe sales volume mismatch type monetary transaction flow pattern mayspecify that earnings and sales reports may be significant. In someimplementations, the earnings and sales reports included in the earningsand sales reports sources may be the only significantextra-transactional information.

For social media sources, the source list specific to the sales volumemismatch type monetary transaction flow pattern may specify one or moreof Yelp® as a social media source, Facebook® as a social media source,Twitter® as a social media source, Google® Maps as a social mediasource, LinkedIn® as a social media source, and/or other sources. Thesource list specific to the sales volume mismatch type monetarytransaction flow pattern may specify one or more of significance of thedifferent social media sources, significance of the types ofextra-transactional information in the social media sources, and/orsignificance of the extra-transactional information. By way ofnon-limiting illustration, the source list specific to the sales volumemismatch type monetary transaction flow pattern may specify that Yelp®may be more significant than Facebook®, Facebook® may be moresignificant than Twitter®, Twitter® may be more significant than Google®Maps, and/or Google® Maps may be more significant than LinkedIn®.

For extra-transactional information included in Yelp®, the source listspecific to the receiving velocity type monetary transaction flowpattern may specify one or more of: quantity of reviews may be moresignificant than average review length, average review length may bemore significant than quantity of photos, quantity of photos may be moresignificant than average photos per review, average photos per reviewmay be more significant than age of profile, age of profile may be moresignificant than distribution of reviews over time, distribution ofreviews over time may be more significant than quantity of stars (orother rating), quantity of stars may be more significant thanidentification information (e.g., address, phone number, website, hours,etc.), the identification information may be more significant thanaverage votes per review, average votes per review may be moresignificant than average quantity of reviews done by individualreviewers, and/or average quantity of reviews done by individualreviewers may be more significant than quantity of check-ins.

For extra-transactional information included in Facebook®, the sourcelist specific to the receiving velocity type monetary transaction flowpattern may specify one or more of: quantity of reviews may be moresignificant than average review length, average review length may bemore significant than quantity of photos, quantity of photos may be moresignificant than quantity of likes (and/or followers and/or visits),quantity of likes (and/or followers and/or visits) may be moresignificant than identification information (including one or more ofaddress, phone number, website, hours, business category, and/or pricerange), the identification information may be more significant thaninformation establishing whether reviewers are real people, informationestablishing whether reviewers are real people may be more significantthan average likes on posts (and/or comments on posts), the averagelikes on posts (and/or comments on posts) may be more significant thanfrequency of posts, and/or the frequency of posts may be moresignificant than age of profile.

For extra-transactional information included in Twitter®, the sourcelist specific to the receiving velocity type monetary transaction flowpattern may specify one or more of: age of profile may be moresignificant than quantity of tweets, and/or quantity of tweets may bemore significant than engagement information (e.g., quantity ofretweets, comments, and/or likes).

For extra-transactional information included in Google® Maps, the sourcelist specific to the receiving velocity type monetary transaction flowpattern may specify one or more of: listed hours may be more significantthan listed address, listed address may be more significant than listedphone number, listed phone number may be more significant than listedwebsite, listed website may be more significant than quantity of review,quantity of reviews may be more significant than average rating, averagerating may be more significant than a quantity of photos, and/orquantity of photos may be more significant than average length ofreviews.

For extra-transactional information included in LinkedIn®, the sourcelist specific to the receiving velocity type monetary transaction flowpattern may specify identification information may be most significant.In some implementations, length of biography on the profile (e.g., an“about us” description) may be more significant than listed website,listed website may be more significant than listed address, listedaddress may be more significant than year founded, year founded may bemore significant than company size, company size may be more significantthan specialty offerings, specialty offerings may be more significantthan job postings, job postings may be more significant than quantity ofnew hires by month, quantity of new hires by month may be moresignificant than total job openings, and/or total job openings may bemore significant than average employee tenure.

Source accessing component 116 may be configured to access the sourcesof one or more sources 120 and obtain the extra-transactionalinformation in the sources related to one or more entities involved inmonetary transactions contributing to the monetary transaction flowpatterns. Source(s) 120 may be accessed and/or extra-transactionalinformation may be obtained over network(s) 122, such as the internet.By way of non-limiting illustration, responsive to obtaining the firstsource list, source accessing component 116 may be configured to accesssources of the first source list are accessed to obtainextra-transactional information about the first entity.

Evaluation component 118 may be configured to evaluate theextra-transactional information included in the sources in accordancewith the indicated significance of the sources. The indicatedsignificance of the sources may convey weights applied to the sources indetermining whether to validate or disprove of the suspicions. Theevaluation may be directed to validating or disproving the suspicions ofthe monetary transaction flow patterns indicated in the alerts.Evaluating the extra-transactional information included in the sourcesmay be further in accordance with one or more of the indicatedsignificance of the types of extra-transactional information included inthe individual sources, the indicated significance of theextra-transactional information included in the individual sources,and/or other information. The indicated significance of the sources mayconvey weights applied to the sources in determining whether to validateor disprove of the suspicions. The indicated significance of the typesof the individual extra-transactional information may convey weightsapplied to the types of the individual extra-transactional informationin determining whether to validate or disprove of the suspicions. Theindicated significance of the individual extra-transactional informationmay convey weights applied to the individual extra-transactionalinformation in determining whether to validate or disprove of thesuspicions.

Evaluation component 118 may be configured to evaluate theextra-transactional information by assigning risk scores to theextra-transactional information included in the sources. Evaluationcomponent 118 may be configured to determine the risk scores assigned tothe extra-transactional information included in the sources byidentifying the extra-transactional information within the sourcesand/or determining if the identified extra-transactional information isconsistent with money laundering. The risk scores may be assigned inaccordance with the indicated significance of the types ofextra-transactional information, the indicated significance of theextra-transactional information, and/or may be determined in other ways.

In some implementations, determining whether extra-transactionalinformation is consistent with money laundering may be based on one ormore of an objective standard, a subjective standard, common practicescarried out by persons skilled in the art of investigating AML alerts, alook-up table specifying if the extra-transactional information may beconsistent with money laundering and/or a score that should be appliedthereto, and/or by other evaluation techniques. Determining whetherextra-transactional information is consistent with money laundering maybe dependent on the pattern type of a monetary transaction flow patternindicated in an alert. For example, an entity conducting business maycarry out monetary transactions that contribute to the varioussuspicious monetary transaction flow patterns described herein. In orderto determine if business they are conducting includes legitimate andlegal transaction or the perpetration of money laundering, looking atextra-transactional information may provide some insight as to whetherthose transactions are legitimate and legal or illegal money laundering.

For example, if a given source is accessed and particularextra-transactional information is found within that source, theextra-transactional information may be assigned a specific risk scorebased on the existence of that particular extra-transactionalinformation within the source. By way of non-limiting illustration, fora receiving velocity type monetary transaction flow pattern indicatingthat a velocity of received moneys has exceeded a threshold, if a creditcard information source includes extra-transactional information showingthat the transaction history has decreased (e.g., the particularextra-transactional information), then this extra-transactionalinformation may be determined as being indicative of money laundering.Further, if transaction history is specified as the most significant forthe credit card information sources, than the transaction history may beassigned a specific risk score that may be relatively higher than otherrisk scores, and/or a risk score may be weighted relatively higher inthe overall evaluation of extra-transactional information from thatsource and/or from other sources. Conversely, for the receiving velocitytype monetary transaction flow pattern indicating that a velocity ofreceived moneys has exceeded a threshold, if a credit card informationsource includes extra-transactional information showing that thetransaction history has also increased, then this extra-transactionalinformation may be determined as not being indicative of moneylaundering. Instead, this extra-transactional information may disprovethe suspicions that increased velocity of received moneys is associatedwith legitimate business transactions. In this case, if transactionhistory is specified as the most significant for the credit cardinformation sources, than the transaction history may be assigned aspecific risk score that may be relatively lower than other risk scores,and/or a risk score may be weighted relatively lower in the overallevaluation of extra-transactional information from that source and/orfrom other sources.

While the above examples illustrates just a few ways thatextra-transactional information may be determined as being indicative ofmoney laundering for the purpose of assigning a risk score, this is forillustrative purposes only and is not to be considered limiting. Forexample, it is to be understood that those skilled in the art mayrecognize other ways in which the extra-transactional information may beevaluated to determine if that information is or is not indicative ofmoney laundering and/or how that evaluation may be used to assign a riskscore. By way of further non-limiting illustration, if a social mediasource indicates that a business entity sending and/or receiving moneydoes not have a listed address, website, phone number, and/or otheridentification information, this may be indicative of the businessentity may be a fraud and used specifically for the concealment ofmoney. A risk score may be assigned to that identification informationthat may convey relatively more risk than other extra-transactionalinformation (e.g., by assigning it a relatively higher risk score).

In some implementations, a risk score may comprise one or more ofquantitative value, a qualitative value, and/or other measure of risk.In some implementations, a risk score may be determined using a lookuptable of risk scores selected for particular extra-transactionalinformation and/or source when extra-transactional information within asource may be determined as being consistent with money laundering.

In some implementations, a source may be initially assigned a base riskscore. The risk score may be increased in response to the identifiedextra-transactional information within that source being consistent withmoney laundering and/or decreased in response to identifiedextra-transactional information within that source being inconsistentwith money laundering. By way of non-limiting illustration, a base riskscore of zero (“0”) and/or other value may be assigned to individualsources. The base risk score may be increased and/or decreased dependingon the finding of extra-transactional information being consistent orinconsistent with money laundering. In some implementations, theincrease or decrease may be by a unit (e.g., by a value of “1”) and/orother manner of increasing and/or decreasing the score. For example,relatively more risk (e.g., based on significance) may result in anincrease or decrease of more than a unit value.

Evaluation component 118 may be configured to evaluate theextra-transactional information by determining individual weighted riskscores and/or other information. The weighted risk scores may a scoreassigned to individual alerts once extra-transactional information hasbeen evaluated. A weighted risk score may be determined by weighting theindividual risk scores determined for sources accessed for a given alertand/or the individual risk scores determine for the extra-transactionalinformation included in the sources. The weighting may be a coefficient.The weighting may be in accordance with the indicated significance ofthe sources and/or other information. In some implementations, theweighting may be value determined using a lookup table conveyingcoefficients of weight that correspond to indicated significance (e.g.,placement on a rank). By way of non-limiting illustration, sourcesranked higher may be weighted by a relative larger coefficient thansources ranked relatively lower.

In some implementations, once risk scores have been assigned toextra-transactional information within a given source, the risk scoresmay be aggregated to determine an overall risk score for that source. Insome implementations, the aggregating of risk scores may be made byaddition and/or other aggregation. In some implementations, theaggregation may utilize weights assigned to individualextra-transactional information. Then, the individual overall riskscores for the individual sources included within a source list may beaggregated to determine the weighted risk score for a given alert. Theaggregation to determine the weighted risk score may utilize the weightsassigned to individual sources based on significance.

The weighted risk scores may convey a likelihood that the suspicions ofan alert may or may not be valid. By way of non-limiting illustration,if the weighted risk score for evaluating a monetary transaction flowpattern indicated in a given alert meets and/or exceeds a thresholdvalue, the suspicions of the alert may be validated. This validation maybe communicated to an authority and/or other entity so that appropriatefurther steps may be conducted by the proper authority or entity.

In some implementations, the threshold value(s) of which the weightedrisk scores may be evaluated against may be one or more of specific tothe pattern type of a given alert, learned over time, and/or preset byan administrator of system 100. In some implementations, the thresholdvalue may be learned by training a machine learning program, such as aneural network and/or other machine learning. For example, a machinelearning program may be trained by providing training data comprisinginputs and known outputs and/or other information. Cases where alertshave been validated or disproved, for example, based on manualevaluation, may be provided into a neural network to train the neuralnetwork to establish threshold values and/or other information. Thealert information for those cases may be provided as inputs andextra-transactional information and/or manually determined risk scoresestablishing the cases as either valid or disproved may be provided asthe desired outputs. It is noted that carrying out other features and/orfunctions of system 100 may be facilitated by machine learning.Different aspects of a machine learning program may be trained accordingto appropriate training data.

By way of non-limiting illustration, evaluating the extra-transactionalinformation included in the sources of the first source list may includeevaluating first extra-transactional information about the first entityincluded a first source of the first source list. The first source mayhave a first level of significance. Evaluation component 118 may beconfigured to evaluate second extra-transactional information about thefirst entity included in a second source of the first source list. Thesecond source may have a second level of significance. Evaluationcomponent 118 may be configured to determine whether to validate ordisprove the suspicions of the first monetary transaction flow patternbased on the evaluations by giving the first extra-transactionalinformation a first weight and the second extra-transactionalinformation a second weight. The first weight may correspond to thefirst level of significance, and the second weight corresponding to thesecond level of significance. A first risk score may be assigned to thefirst extra-transactional information and a second risk score isassigned to the second extra-transactional information. A first weightedrisk score may be determined for the first monetary transaction flowpattern based on weighting the first risk score by the first weight andthe second risk score by the second weight. The validation or disprovalof the suspicions of the first monetary transaction flow pattern may bebased on the first weighted risk score.

In some implementations, server(s) 102, client computing platform(s)104, and/or external resources 126 may be operatively linked via one ormore electronic communication links. For example, such electroniccommunication links may be established, at least in part, via a networksuch as the Internet and/or other networks. It will be appreciated thatthis is not intended to be limiting, and that the scope of thisdisclosure includes implementations in which server(s) 102, clientcomputing platform(s) 104, and/or external resources 126 may beoperatively linked via some other communication media.

A given client computing platform 104 may include one or more processorsconfigured to execute computer program modules. The computer programmodules may be configured to enable an expert or user associated withthe given client computing platform 104 to interface with system 100and/or external resources 126, and/or provide other functionalityattributed herein to client computing platform(s) 104. By way ofnon-limiting example, the given client computing platform 104 mayinclude one or more of a desktop computer, a laptop computer, a handheldcomputer, a tablet computing platform, a NetBook, a Smartphone, a gamingconsole, and/or other computing platforms.

External resources 126 may include sources of information outside ofsystem 100, external entities participating with system 100, and/orother resources. In some implementations, some or all of thefunctionality attributed herein to external resources 126 may beprovided by resources included in system 100.

Server(s) 102 may include electronic storage 128, one or more processors130, and/or other components. Server(s) 102 may include communicationlines, or ports to enable the exchange of information with a networkand/or other computing platforms. Illustration of server(s) 102 in FIG.1 is not intended to be limiting. Server(s) 102 may include a pluralityof hardware, software, and/or firmware components operating together toprovide the functionality attributed herein to server(s) 102. Forexample, server(s) 102 may be implemented by a cloud of computingplatforms operating together as server(s) 102.

Electronic storage 128 may comprise non-transitory storage media thatelectronically stores information. The electronic storage media ofelectronic storage 128 may include one or both of system storage that isprovided integrally (i.e., substantially non-removable) with server(s)102 and/or removable storage that is removably connectable to server(s)102 via, for example, a port (e.g., a USB port, a firewire port, etc.)or a drive (e.g., a disk drive, etc.). Electronic storage 128 mayinclude one or more of optically readable storage media (e.g., opticaldisks, etc.), magnetically readable storage media (e.g., magnetic tape,magnetic hard drive, floppy drive, etc.), electrical charge-basedstorage media (e.g., EEPROM, RAM, etc.), solid-state storage media(e.g., flash drive, etc.), and/or other electronically readable storagemedia. Electronic storage 128 may include one or more virtual storageresources (e.g., cloud storage, a virtual private network, and/or othervirtual storage resources). Electronic storage 128 may store softwarealgorithms, information determined by processor(s) 130, informationreceived from server(s) 102, information received from client computingplatform(s) 104, and/or other information that enables server(s) 102 tofunction as described herein.

Processor(s) 130 may be configured to provide information processingcapabilities in server(s) 102. As such, processor(s) 130 may include oneor more of a digital processor, an analog processor, a digital circuitdesigned to process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information. Although processor(s) 130 is shown in FIG. 1 asa single entity, this is for illustrative purposes only. In someimplementations, processor(s) 130 may include a plurality of processingunits. These processing units may be physically located within the samedevice, or processor(s) 130 may represent processing functionality of aplurality of devices operating in coordination. Processor(s) 130 may beconfigured to execute modules 110, 112, 114, 116, 118, and/or othermodules. Processor(s) 130 may be configured to execute modules 110, 112,114, 116, 118, and/or other modules by software; hardware; firmware;some combination of software, hardware, and/or firmware; and/or othermechanisms for configuring processing capabilities on processor(s) 130.As used herein, the term “module” may refer to any component or set ofcomponents that perform the functionality attributed to the module. Thismay include one or more physical processors during execution ofprocessor readable instructions, the processor readable instructions,circuitry, hardware, storage media, or any other components.

It should be appreciated that although modules 110, 112, 114, 116, and118 are illustrated in FIG. 1 as being implemented within a singleprocessing unit, in implementations in which processor(s) 130 includesmultiple processing units, one or more of modules 110, 112, 114, 116,and/or 118 may be implemented remotely from the other modules. Thedescription of the functionality provided by the different modules 110,112, 114, 116, and/or 118 described below is for illustrative purposes,and is not intended to be limiting, as any of modules 110, 112, 114,116, and/or 118 may provide more or less functionality than isdescribed. For example, one or more of modules 110, 112, 114, 116,and/or 118 may be eliminated, and some or all of its functionality maybe provided by other ones of modules 110, 112, 114, 116, and/or 118. Asanother example, processor(s) 130 may be configured to execute one ormore additional modules that may perform some or all of thefunctionality attributed below to one of modules 110, 112, 114, 116,and/or 118.

FIG. 2 illustrates a method 200 to facilitate automated validation ofanti-money laundering alerts, in accordance with one or moreimplementations. The operations of method 200 presented below areintended to be illustrative. In some implementations, method 200 may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of method 200 are illustrated in FIG.2 and described below is not intended to be limiting.

In some implementations, method 200 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The one or moreprocessing devices may include one or more devices executing some or allof the operations of method 200 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of method 200.

An operation 202 may include obtaining alert information and/or otherinformation. The alert information may include one or more of alertsindicating monetary transaction flow patterns suspected as moneylaundering, indications of entities involved in monetary transactionsthat contribute to the monetary transaction flow patterns, and/or otherinformation. By way of non-limiting illustration, alert information maybe obtain including a first alert indicating a first monetarytransaction flow pattern suspected as money laundering, a first entityinvolved in one or more monetary transactions that contribute to thefirst monetary transaction flow pattern, and/or other information.Operation 202 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a component thatis the same as or similar to alert component 110, in accordance with oneor more implementations.

An operation 204 may include identifying pattern types of the monetarytransaction flow patterns, including identifying a first pattern type ofthe first monetary transaction flow pattern. Operation 204 may beperformed by one or more hardware processors configured bymachine-readable instructions including a component that is the same asor similar to pattern type component 112, in accordance with one or moreimplementations.

An operation 206 may include obtaining source lists indicating sourcesof extra-transactional information. An individual source list may bespecific to an individual pattern type. The extra-transactionalinformation may include information about the entities involved in themonetary transactions. An individual source list may further indicatesignificance of the individual sources included in the individual sourcelist. A first source list may be obtained responsive to identifying thefirst pattern type. The first source list may be specific to the firstpattern type. Operation 206 may be performed by one or more hardwareprocessors configured by machine-readable instructions including amodule that is the same as or similar to source list component 114, inaccordance with one or more implementations.

An operation 208 may include accessing the sources and obtaining theextra-transactional information in the sources that may be related tothe one or more entities involved in the monetary transactions. By wayof non-limiting illustration, responsive to obtaining the first sourcelist, sources of the first source list may be accessed to obtainextra-transactional information about the first entity. Operation 208may be performed by one or more hardware processors configured bymachine-readable instructions including a component that is the same asor similar to source accessing component 116, in accordance with one ormore implementations.

An operation 210 may include evaluating the extra-transactionalinformation included in the sources in accordance with the indicatedsignificance of the sources to validate or disprove the suspicions ofthe monetary transaction flow patterns indicated in the alerts. Theindicated significance of the sources may convey weights applied to thesources in determining whether to validate or disprove of thesuspicions. By way of non-limiting illustration, evaluating theextra-transactional information included in the sources of the firstsource list may include evaluating first extra-transactional informationabout the first entity included a first source of the first source list.The first source may have a first level of significance. Evaluating theextra-transactional information included in the sources of the firstsource list may include evaluating second extra-transactionalinformation about the first entity included in a second source of thefirst source list. The second source may have a second level ofsignificance. Determining whether to validate or disprove the suspicionsof the first monetary transaction flow pattern may be based on theevaluations by giving the first extra-transactional information a firstweight and the second extra-transactional information a second weight.The first weight may correspond to the first level of significance. Thesecond weight may correspond to the second level of significance.Operation 210 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a component thatis the same as or similar to evaluation component 118, in accordancewith one or more implementations.

Although the present technology has been described in detail for thepurpose of illustration based on what is currently considered to be themost practical and preferred implementations, it is to be understoodthat such detail is solely for that purpose and that the technology isnot limited to the disclosed implementations, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended claims. For example, it isto be understood that the present technology contemplates that, to theextent possible, one or more features of any implementation can becombined with one or more features of any other implementation.

What is claimed is:
 1. A system configured to facilitate automatedvalidation of anti-money laundering alerts, the system comprising: oneor more hardware processors configured by machine-readable instructionsto: obtain alert information, the alert information including alertsindicating monetary transaction flow patterns suspected as moneylaundering and entities involved in monetary transactions thatcontribute to the monetary transaction flow patterns, the alertinformation including a first alert, the first alert indicating a firstmonetary transaction flow pattern suspected as money laundering and afirst entity involved in one or more monetary transactions thatcontribute to the first monetary transaction flow pattern; identifypattern types of the monetary transaction flow patterns, includingidentifying a first pattern type of the first monetary transaction flowpattern; obtain source lists indicating sources of extra-transactionalinformation, an individual source list being specific to an individualpattern type, the extra-transactional information including informationabout the entities involved in the monetary transactions, wherein anindividual source list further indicates significance of the individualsources included in the individual source list, such that a first sourcelist is obtained responsive to identifying the first pattern type, thefirst source list being specific to the first pattern type; access thesources and obtaining the extra-transactional information in the sourcesthat is related to the entities involved in the monetary transactions,such that responsive to obtaining the first source list, sources of thefirst source list are accessed to obtain extra-transactional informationabout the first entity; and evaluate the extra-transactional informationincluded in the sources in accordance with the indicated significance ofthe sources to validate or disprove the suspicions of the monetarytransaction flow patterns indicated in the alerts, the indicatedsignificance of the sources conveying weights applied to the sources indetermining whether to validate or disprove of the suspicions, such thatevaluating the extra-transactional information included in the sourcesof the first source list comprises: evaluating first extra-transactionalinformation about the first entity included a first source of the firstsource list, the first source having a first level of significance;evaluating second extra-transactional information about the first entityincluded in a second source of the first source list, the second sourcehaving a second level of significance; and determining whether tovalidate or disprove the suspicions of the first monetary transactionflow pattern based on the evaluations by giving the firstextra-transactional information a first weight and the secondextra-transactional information a second weight, the first weightcorresponding to the first level of significance, and the second weightcorresponding to the second level of significance.